ggplot2

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# Using the _tidyverse_

### Maximilian H.K. Hesselbarth

#### University of Michigan (EEB)

2022-03-25

---

# The _tidyverse_

.pull-left[

.ref[www.tidyverse.org]

]

.pull-right[

.ref[Wickham, H., Grolemund, G., 2016. R for Data Science, 1st ed. O’Reilly, Newton (USA).]

]

---

# Tidy data

1. Each **variable** forms a **column**.

2. Each **observation** forms a **row**.

3. Each **value** must have its own **cell**.

.ref[Wickham, H., 2014. Tidy Data. Journal of Statistical Software 59, 1–23. https://doi.org/10.18637/jss.v059.i10]

.ref[Illustration from the Openscapes blog Tidy Data for reproducibility, efficiency, and collaboration by Julia Lowndes and Allison Horst]

---

# Tidy data

.ref[Wickham, H., Grolemund, G., 2016. R for Data Science, 1st ed. O’Reilly, Newton (USA).]

---

# Install and load

```r
install.packages("tidyverse")
```

```r
library(tidyverse)
```

```
## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.1 ──
```

```
## ✓ ggplot2 3.3.5     ✓ purrr   0.3.4
## ✓ tibble  3.1.6     ✓ dplyr   1.0.8
## ✓ tidyr   1.2.0     ✓ stringr 1.4.0
## ✓ readr   2.1.2     ✓ forcats 0.5.1
```

```
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## x dplyr::filter() masks stats::filter()
## x dplyr::lag()    masks stats::lag()
```

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# Core packages

`readr`   : Read rectangular data

`tibble`  : Modern re-imagining of data frames

`stringr` : Functions to work with strings (i.e. sequence of characters)

`forcats` : Functions to modify factors (i.e. categorical data)

`tidyr`   : Functions to tidy/reshape data

`dplyr`   : Functions for data manipulation

`purrr`   : Functional programming

`ggplot2` : Data visualization

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# Pipe operator `%>%`

- `f(x)` is equivalent to `x %>% f`

- `f(x, y)` is equivalent to `x %>% f(y)`

- `f(y, x)` is equivalent to `x %>% f(y, .)`

```r
set.seed(42)
x <- runif(n = 10)

*min(log(sort(x)))
## [1] -2.004953

*x %>% sort() %>% log() %>% min()
## [1] -2.004953

set.seed(42)
*10 %>% runif() %>% sort() %>% log() %>% min()
## [1] -2.004953
```

---

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# Palmer penguins dataset

.ref[Horst A.M., Hill A.P., Gorman K.B., 2020. palmerpenguins: Palmer Archipelago (Antarctica) penguin data. R package version 0.1.0.]

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# Read data: _readr_

- Reads automatically into `tibble`

- Different `readr::read_*()` functions for different data types

- Functions to write data `readr::write_*()`

- `readxl` as alternative for Excel data

```r
*df_penguins <- readr::read_csv("data/penguins_raw.csv") %>%
  magrittr::set_names(names(.) %>% stringr::str_remove_all(pattern = " ") %>% 
                        stringr::str_remove_all(pattern = "\$[^()]+\$"))
## Rows: 344 Columns: 17
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr  (9): studyName, Species, Region, Island, Stage, Individual ID, Clutch C...
## dbl  (7): Sample Number, Culmen Length (mm), Culmen Depth (mm), Flipper Leng...
## date (1): Date Egg
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
```

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# Tidy data: _tidyr_

- Some functions to deal with `NA` values

```r
df_penguins <- tidyr::drop_na(df_penguins, BodyMass)
```

- `nest()`/`unnest()` to organize data

```r
tidyr::nest(df_penguins, data = -studyName)
```

```
## # A tibble: 3 × 2
##   studyName data               
##   <chr>     <list>             
## 1 PAL0708   <tibble [109 × 16]>
## 2 PAL0809   <tibble [114 × 16]>
## 3 PAL0910   <tibble [119 × 16]>
```

- `pivot_longer()` & `pivot_wider()` most important functions to **reshape** data

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# Wrangle data: _dplyr_

- `filter()` to subset **rows**; `select()` to subset **columns**

```r
dplyr::filter(df_penguins, BodyMass >= quantile(BodyMass, 0.75), Sex != "FEMALE") %>% 
  dplyr::select_if(is.numeric) %>% head(5)
```

```
## # A tibble: 5 × 7
##   SampleNumber CulmenLength CulmenDepth FlipperLength BodyMass Delta15N Delta13C
##          <dbl>        <dbl>       <dbl>         <dbl>    <dbl>    <dbl>    <dbl>
## 1          110         43.2        19             197     4775     9.32    -25.5
## 2            2         50          16.3           230     5700     8.15    -25.4
## 3            4         50          15.2           218     5700     8.26    -25.4
## 4            5         47.6        14.5           215     5400     8.23    -25.5
## 5            8         46.7        15.3           219     5200     8.23    -25.4
```

- `pull()` to convert **one** column as vector

```r
dplyr::pull(df_penguins, FlipperLength) %>% head(10)
```

```
##  [1] 181 186 195 193 190 181 195 193 190 186
```

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# Wrangle data: _dplyr_

- `mutate()` to **create/modify** columns

- `case_when()` as vectorised **ifelse** statements

- `slice()` to subset **rows** by position

```r
dplyr::select(df_penguins, IndividualID, Species, CulmenLength) %>%
* dplyr::mutate(CulmenLength_cm = CulmenLength / 10,
                CulmenLenth_class = 
                  dplyr::case_when(CulmenLength < 35  ~ "small",
*                                  CulmenLength >= 35 & CulmenLength <= 45 ~ "med",
                                   CulmenLength > 45 ~ "large")) %>%
  dplyr::slice(sample(1:nrow(.), size = 3))
```

```
## # A tibble: 3 × 5
##   IndividualID Species             CulmenLength CulmenLength_cm CulmenLenth_cla…
##   <chr>        <chr>                      <dbl>           <dbl> <chr>           
## 1 N62A1        Chinstrap penguin …         46.4            4.64 large           
## 2 N13A1        Adelie Penguin (Py…         38.8            3.88 med             
## 3 N92A1        Chinstrap penguin …         45.7            4.57 large
```

---

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# Wrangle data: _dplyr_

- `group_by()` to group by **column**

- `summarise()` to **summarize** each group

- `n()` to **count** observations within each group (context dependent)

```r
*df_penguins_sum <- dplyr::group_by(df_penguins, Island, Species) %>%
* dplyr::summarise(n = dplyr::n(),
                   FlipperLength_m = mean(FlipperLength), 
                   FlipperLength_sd = sd(FlipperLength), 
*                  .groups = "drop")
```

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# Wrangle data: _dplyr_

- `*_join()` to combine columns from `x` and `y` using matching **keys**

```r
*dplyr::left_join(x = df_penguins, y = df_penguins_sum, by = c("Island", "Species")) %>%
  dplyr::select(Species, Island, tidyselect::starts_with("Flipper")) %>% 
  dplyr::slice(sample(1:nrow(.), size = 5))
```

```
## # A tibble: 5 × 5
##   Species                  Island FlipperLength FlipperLength_m FlipperLength_sd
##   <chr>                    <chr>          <dbl>           <dbl>            <dbl>
## 1 Adelie Penguin (Pygosce… Dream            190            190.             6.59
## 2 Gentoo penguin (Pygosce… Biscoe           213            217.             6.48
## 3 Adelie Penguin (Pygosce… Biscoe           198            189.             6.73
## 4 Adelie Penguin (Pygosce… Biscoe           174            189.             6.73
## 5 Chinstrap penguin (Pygo… Dream            187            196.             7.13
```

.ref[https://statisticsglobe.com/r-dplyr-join-inner-left-right-full-semi-anti]

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# Functional programming: _purrr_

- `map_*()` to apply function to each element (vector/list)

```r
species_names <- unique(df_penguins$Species)
*purrr::map(species_names, function(i) {
  dplyr::filter(df_penguins, Species == i) %>% dplyr::pull(Island) %>% 
    unique() %>% stringr::str_sort() %>% paste(collapse = ", ")})
```

```
## [[1]]
## [1] "Biscoe, Dream, Torgersen"
## 
## [[2]]
## [1] "Biscoe"
## 
## [[3]]
## [1] "Dream"
```

```r
*purrr::map_int(species_names, function(i) {
  dplyr::filter(df_penguins, Species == i) %>% dplyr::pull(Island) %>% 
    unique %>% length})
```

```
## [1] 3 1 1
```

---

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## Thank you for your attention

### Questions?

.pull-left[

Further resources: [https://mhesselbarth.github.io/advanced-r-workshop/resources](https://mhesselbarth.github.io/advanced-r-workshop/resources)

Exercise: [https://mhesselbarth.github.io/advanced-r-workshop/exercise_tidyverse](https://mhesselbarth.github.io/advanced-r-workshop/exercise_tidyverse)

]

.pull-right[

]